The SP algorithm requires the voters to predict other voters' report in the form of a full probability distribution over all rankings of alternatives.

Ateachh2[H] inanepisodek, thealgorithmsh, k, selects ah, k h, k(sh, k, ), and costsrh(sh, k,ah, k)andch(sh, k,ah, k). Wewillalsoshowthat k from (10) (onceitbecomes feasible) willindeedbeasafepolicy (see Proposition 5).

High sample complexity has long been a challenge for RL.

In this paper, we present VoiceMixer which can effectively decompose and transfer voice style through a novel information bottleneck and adversarial feedback.

Figure 1: (left) Venn diagram of the new types of random features (green) we propose.(right)

Linear attention methods offer Transformers $O(N)$ complexity but typically underperform standard softmax attention. We identify two fundamental limitations affecting these approaches: the restriction to convex combinations that only permits additive information blending, and uniform accumulated weight bias that dilutes attention in long contexts. We propose Zero-Sum Linear Attention (ZeroS), which addresses these limitations by removing the constant zero-order term $1/t$ and reweighting the remaining zero-sum softmax residuals. This modification creates mathematically stable weights, enabling both positive and negative values and allowing a single attention layer to perform contrastive operations. While maintaining $O(N)$ complexity, ZeroS theoretically expands the set of representable functions compared to convex combinations. Empirically, it matches or exceeds standard softmax attention across various sequence modeling benchmarks.